image/svg+xml Sukhbaatar BatchuluunSupervisor: Koichiro ShiomoriInterdisciplinary Graduate School of Agriculture and Engineering, University of Miyazaki 宮崎大学 University of Miyazaki Preparation of polystyrene microcapsules containing water droplets by solvent evaporation method and their structural distribution analysis by machine learning 1.Introduction 2.Methods 3.Result and discusions 4.Conclusion - Monocore, multicore, and other aggregated structures were observed on prepared microcapsules. - The structures were automatically detected by the Hough transformation and were classified by SVM.- The structural distribution of the microcapsules prepared at a high weight ratio of solid to the organic phase and a lower ratio of that were analyzed based on classification results. - The monocore structure was dominant at the high ratio in the preparation conditions and otherwise, the multicore was dominant. Microcapsule ? MachineLearning Further work Microcapsule Concrete Self sealing 4em05 4em04 0.050.100.300.50 0 25 50 75 100 125 0 25 50 75 100 125 Log(Diameter) Count Microcapsule structure Monocore Multicore Others S:O=2 S:O=3 4em05 4em04 0.050.100.300.50 0 25 50 75 100 125 0 25 50 75 100 125 Log(Diameter) Count Microcapsule structure Monocore Multicore Others 81% 77% ClassifierkNN kNN kNN kNN kNN kNN SVM SVM SVM SVM SVM SVM Color binarybinarygray gray binarybinarybinarybinarygray gray binarybinary Shape circle rectangle circle rectangle uniform-Histogramdense-Histogram circle rectangle circle rectangle uniform-Histogramdense-Histogram Accuracy, %80.83 64.27 82.57 59.91 40.96 69.72 71.24 51.85 83.01 45.32 55.77 65.36 Table 1. Accuracy of the proposed feature extraction techniques. Figure 2. Microcapsule structure. S-SEM, D-digital microscopy,0-Multicore, (Class 1)1-Monocore, (Class 2)2-Others aggregates (Class 3)
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